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Open Access Research article

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

Wenting Li1, Rui Wang2, Linfu Bai1, Zhangming Yan1 and Zhirong Sun1*

Author Affiliations

1 MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Bioinformatics and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China

2 Computational Biology and Bioinformatics Program, Institute for Genome Science and Policy, Duke University Medical Center, Durham, NC, USA

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BMC Systems Biology 2012, 6:64  doi:10.1186/1752-0509-6-64

Published: 12 June 2012

Abstract

Background

Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples.

Results

In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types.

Conclusions

Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.

Keywords:
Cancer core modules; Genotype-phenotype correlation; Network analysis